NAS is the automated process of finding the most optimal neural network within a neural network search space given target architecture and network architecture constraints, achieving a balance between accuracy, latency, and energy usage [1]}[2]}[3]}. There are three key elements in a hardware-aware NAS pipeline, namely the search strategy and target hardware profiling. The neural network search space can be represented as layer-wise, cell-wise, or hierarchical [4]} Recent NAS techniques can also simultaneously co-design neural network and inference hardware to maximize both efficiency and performance, thereby bridging the gap between traditional NAS and hardware design for neural network inference [5]}[6]}.
